Triple
T9781572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angelo Eugene Ossoli |
E237386
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Angelo |
E34007
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Angelo | Statement: [Angelo Eugene Ossoli, givenName, Angelo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Angelo Context triple: [Angelo Eugene Ossoli, givenName, Angelo]
-
A.
Angelo
chosen
Angelo is a masculine given name of Greek origin, commonly used in various cultures and often associated with the meaning "angel" or "messenger."
-
B.
Angelino
Angelino is an informal term for a resident of Los Angeles, California.
-
C.
Maschio Angioino
Maschio Angioino is a historic medieval and Renaissance fortress in Naples, Italy, renowned as a former royal residence and one of the city’s most iconic landmarks.
-
D.
Montardo
Montardo is a prominent mountain peak in the central Pyrenees, known for its panoramic views over the Val d’Aran in Catalonia, Spain.
-
E.
Nicolangelo Carnimeo
Nicolangelo Carnimeo was an Italian military officer best known for commanding Italian forces during the World War II Battle of Keren in East Africa.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca84da927881909bda80caecad6010 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda1b23cb88190b458ab18d5f7f493 |
completed | April 1, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1bd2e5f4c81908a3c132df6440947 |
completed | April 5, 2026, 1:38 a.m. |
Created at: March 30, 2026, 8:27 p.m.